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A novel deep learning approach for the detection and classification of lung nodules from CT images

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Abstract

In a conventional Computer-Aided Detection (CAD) system, complexity is seen in the classification procedure of Lung Nodule Detection (LND). Lower classification accuracy along with a high False-Positive Rate (FPR)is caused since the classification outcome extremely relies on the performance of every step in LND. The work proposed a new Deep Learning (DL) approach for detecting and classifying Lung Nodules (LNs) from Computer Tomography (CT) images to address these difficulties. Initially, the input lung image is pre-processed, and then the non-informatics blocks are removed using Step Deviation Mean Multilevel Thresholding (SDMMT). After that, the lung image’s contrast is enriched and the earliest event-Net classifier is utilized to detect the LN parts. From the identified LN portion, the features are retrieved and the important features are chosen using an optimization algorithm called Minkowski Distance-based Horse herd optimization Algorithm (MD-HHOA). The selected features are fed into the Crossover Swap-Displacement and Reversion-based Jaya-Weight Hinge Generative Adversarial Network (CSDR-J-WHGAN) classifier for classifying as nodule or non-nodule. This study utilizes publicly accessible Lung Image Database Consortium image collection (LIDC-IDRI) datasets.The experiential result shows that the proposed method attains 97.11% accuracy, 96.98% sensitivity, and 94.34% specificity for detecting nodules when compared with the existing methods.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

We thank the anonymous referees for their useful suggestions.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, KL Deemed to be University, Hyderabad, Telangana, India

    Vijay Kumar Gugulothu

  2. Department of Computer Science & Engineering, Government Polytechnic, Hyderabad, Masab Tank, India

    Savadam Balaji

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Vijay Kumar Gugulothu, Dr.SavadamBalaji. The first draft of the manuscript was written by Vijay Kumar Gugulothu.

and all authors commented on previous versions of the manuscript.All authors read and approved the final manuscript.

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Correspondence to Vijay Kumar Gugulothu.

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Gugulothu, V.K., Balaji, S. A novel deep learning approach for the detection and classification of lung nodules from CT images. Multimed Tools Appl 82, 47611–47634 (2023). https://doi.org/10.1007/s11042-023-15416-8

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  • DOI: https://doi.org/10.1007/s11042-023-15416-8

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